Objective: To study the pharmacological effects of Zengxuean capsule (Radix Astragali, Indigo Naturalis, Cordyceps, etc) on anti inflammation.Methods:A few conventional models, such as the mouse pinna swelling induced by croton oil, were employed to determine the anti inflammatory activities of Zengxuean capsule.Results: Zengxuean capsule obviously inhibited the swelling of mouse pinna induced by croton oil, suppressed the edema of rat hind paw elicited by injection of 1% carrageenan, significantly depressed the increase of peritoneal capillary permeability in the xylene induced mice, and greatly decreased proliferation of granuloma led by implanting cotton pellet. It had an abatement effect of algesia induced by heat with tail flick method, and could not inhibit the swelling of pinna led by croton oil when mouse's adrenal was resected.Conclusion: Zengxuean capsule is an effective medicine on anti inflammation, and its acitivity is realized by adrenal cortical system.

Objective:To explore the relationship between work connectivity behavior after-hours, self-regulatory fatigue and work engagement of nurses, so as to provide reference for developing strategies to improve nurses′work engagement.Methods:This was a cross-sectional survey study. A total of 661 nurses from 10 hospitals in Shandong Province were selected as subjects by convenience sampling method from June to August 2022. The Work Connectivity Behavior After-hours Scale, Self-Regulatory Fatigue Scale and Work Engagement Scale were used to measure the work connectivity behavior after-hours, self-regulatory fatigue level and work engagement level. A structural equation model was constructed to evaluate the mediating effect of self-regulatory fatigue on work connectivity behavior after-hours and work engagement.Results:The scores of work connectivity behavior after-hours, self-regulatory fatigue and work engagement of nurses were (37.66 ± 7.05), (42.98 ± 10.55) and (34.29 ± 6.58) points, respectively. The total effect of work connectivity behavior after hours on work engagement was -0.336 6. The work connectivity behavior after-hours was positively correlated with self-regulatory fatigue ( r = 0.423, P<0.01) and work engagement was negatively correlated with work connectivity behavior after-hours and self-regulatory fatigue ( r = -0.361, -0.479, both P<0.01). Self-regulatory fatigue had a partial mediating effect on work connectivity behavior after-hours and work engagement, accounting for 46.61% of the total effect. Conclusions:Self-regulatory fatigue has a mediating effect on the relationship between nurses′ work connectivity behavior after-hours and work engagement. Nursing managers should reduce the degree of self-regulatory fatigue of nurses by preventing their work connectivity behavior after-hours, so as to improve the work involvement level of nurses.

AIM : To investigate the effects of Ca 2+ /CaM dependent calcineurin(CaN) signaling pathway on cardiomyocytes hypertrophy of rat induced by neuropeptide Y(NPY). METHODS : Cardiomyocytes of neonatal Wistar rats were cultured with NPY of various concentrations (10,100 nmol?L -1 ). Cyclosporine A (CsA) was used to inhibit the activity of CaN. The methods of 3H Leu incorporation was used to assess protein synthesis rate in cardiomyocytes. Western blot and histochemistry were used to measure CaN protein expression and CaN activity in cardiomyocytes. RESULTS : 3 H Leu incorporation of cardiomyocytes were increased significantly by 100 nmol?L -1 NPY ( P

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Background: This study aimed to develop a diabetic kidney disease (DKD) prediction model using long short term memory (LSTM) neural network and evaluate its performance using accuracy, precision, recall, and area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Methods: The study identified DKD risk factors through literature review and physician focus group, and collected 7 years of data from 6,040 type 2 diabetes mellitus patients based on the risk factors. Pytorch was used to build the LSTM neural network, with 70% of the data used for training and the other 30% for testing. Three models were established to examine the impact of glycosylated hemoglobin (HbA1c), systolic blood pressure (SBP), and pulse pressure (PP) variabilities on the model’s performance. Results: The developed model achieved an accuracy of 83% and an AUC of 0.83. When the risk factor of HbA1c variability, SBP variability, or PP variability was removed one by one, the accuracy of each model was significantly lower than that of the optimal model, with an accuracy of 78% (P<0.001), 79% (P<0.001), and 81% (P<0.001), respectively. The AUC of ROC was also significantly lower for each model, with values of 0.72 (P<0.001), 0.75 (P<0.001), and 0.77 (P<0.05). Conclusion The developed DKD risk predictive model using LSTM neural networks demonstrated high accuracy and AUC value. When HbA1c, SBP, and PP variabilities were added to the model as featured characteristics, the model’s performance was greatly improved.

Objective    To investigate effectiveness and safety of transcatheter aortic valve replacement in the treatment of aortic regurgitation. Methods     PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, Wanfang Data and VIP were searched from inception to August 2021. According to the criteria of inclusion and exclusion, two reviewers independently screened the literature, extracted the data and evaluated the quality of the included studies. Then, Stata 16.0 software was used for meta-analysis. Subgroup meta-analysis of valve type used and study type was performed. Results    Twenty-five studies (12 cohort studies and 13 single-arm studies) were included with 4 370 patients. Meta-analysis results showed that an incidence of device success was 87% (95%CI 0.81-0.92). The success rate of the new generation valve subgroup was 93% (95%CI 0.89-0.96), and the early generation valve subgroup was 66% (95%CI 0.56-0.75). In addition, the 30-day all-cause mortality was 7% (95%CI 0.05-0.10), the 30-day cardiac mortality was 4% (95%CI 0.01-0.07), the incidence of pacemaker implantation was 10% (95%CI 0.08-0.13), and the incidence of conversion to thoraco-tomy was 2% (95%CI 0.01-0.04). The incidence of moderate or higher paravalvular aortic regurgitation was 6% (95%CI  0.03-0.09). Conclusion     Transcatheter aortic valve replacement for aortic regurgitation is safe and yields good results, but some limitations can not be overcome. Therefore, multicenter randomized controlled trials are needed to confirm our results.

Objective    To systematically review the efficacy and safety of transfemoral transcatheter aortic valve replacement (TFTAVR) under local anesthesia (LA) and general anesthesia (GA). Methods    Electronic databases including PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang and CBM were searched to collect randomized controlled trial and cohort studies on clinical outcomes of TFTAVR under LA and GA from inception to September 2020. Two authors independently screened literature, extracted data and assessed the quality of studies, and a meta-analysis was performed by using Stata 16.0 software. Results    A total of 30 studies involving 52 087 patients were included in this study. There were 18 719 patients in the LA group and 33 368 patients in the GA group. The results of meta-analysis showed that the in-hospital all-cause mortality rate [RR=0.65, 95%CI (0.45, 0.94), P=0.021], 30-day all-cause mortality rate [RR=0.73, 95%CI (0.62, 0.86), P<0.001], 30-day stroke [RR=0.82, 95%CI (0.68, 0.98), P=0.025], cardiac arrest [RR=0.50, 95%CI (0.34, 0.73), P<0.001], ICU stay time [RR=−6.86, 95%CI (−12.31, −1.42), P=0.013], and total hospital stay  time [RR=−2.02, 95%CI (−2.59, −1.45), P<0.001] in the LA group were all better than those in the GA group. There was no significant difference in the in-hospital stroke [RR=0.83, 95%CI (0.69, 1.00), P=0.053], in-hospital myocardial infarction (MI) [RR=1.74, 95%CI (0.43, 7.00), P=0.434], or 30-day MI [RR=0.77, 95%CI (0.42, 1.42), P=0.404] between the two groups. Conclusion    LA provides a safe and effective way to induce sedation without intubation, and may be a good alternative to GA for TFTAVR.